1,893 research outputs found
InfoFair 2010- Kenneth D. Mandl, M.D., M.P.H. Lecture: "Patients at the Center of an Innovation Platform: Personally Controlled Health Records and the App Store for Health"
No full textKenneth D. Mandl, M.D., M.P.H. Presenting "Patients at the Center of an Innovation Platform: Personally Controlled Health Records and the App Store for Health." InfoFair 2010- "The Personal Health Record: Possible, Portable, Private?" Wednesday, December 1, 2010
Temporal Patterns of Medications Dispensed to Children and Adolescents in a National Insured Population
No full textThis study aimed to comprehensively describe prevalence and temporal dispensing patterns for medications prescribed to children and adolescents in the United States. Participants were 1.6 million children (49% female) under 18 years old enrolled in a nation-wide, employer-provided insurance plan. All medication claims from 1999–2006 were reviewed retrospectively. Drugs were assigned to 16 broad therapeutic categories. Effects of trend over time, seasonality, age and gender on overall and within category prevalence were examined. Results: Mean monthly prevalence for dispensed medications was 23.5% (range 19.4–27.5), with highest rates in winter and lowest in July. The age group with the highest prevalence was one-year-old children. On average each month, 17.1% of all children were dispensed a single drug and 6.4% were dispensed two or more. Over time, prevalence for two or more drugs did not change, but the proportion of children dispensed a single drug decreased (slope -.02%, p = .001). Overall, boys had higher monthly rates than girls (average difference 0.9%, p = .002). However, differences by gender were greatest during middle childhood, especially for respiratory and central nervous system agents. Contraceptives accounted for a large proportion of dispensed medication to older teenage girls. Rates for the drugs with the highest prevalence in this study were moderately correlated (average Pearson r.66) with those from a previously published national survey. Conclusion: On average, nearly one quarter of a population of insured children in the United States was dispensed medication each month. This rate decreased somewhat over time, primarily because proportionally fewer children were dispensed a single medication. The rate for two or more drugs dispensed simultaneously remained steady
Comparative Effectiveness Research: An Empirical Study of Trials Registered in ClinicalTrials.gov
No full textBackground
The $1.1 billion investment in comparative effectiveness research will reshape the evidence-base supporting decisions about treatment effectiveness, safety, and cost. Defining the current prevalence and characteristics of comparative effectiveness (CE) research will enable future assessments of the impact of this program.
Methods
We conducted an observational study of clinical trials addressing priority research topics defined by the Institute of Medicine and conducted in the US between 2007 and 2010. Trials were identified in ClinicalTrials.gov. Main outcome measures were the prevalence of comparative effectiveness research, nature of comparators selected, funding sources, and impact of these factors on results.
Results
231 (22.3%; 95% CI 19.8%–24.9%) studies were CE studies and 804 (77.7%; 95% CI, 75.1%–80.2%) were non-CE studies, with 379 (36.6%; 95% CI, 33.7%–39.6%) employing a placebo control and 425 (41.1%; 95% CI, 38.1%–44.1%) no control. The most common treatments examined in CE studies were drug interventions (37.2%), behavioral interventions (28.6%), and procedures (15.6%). Study findings were favorable for the experimental treatment in 34.8% of CE studies and greater than twice as many (78.6%) non-CE studies (P<0.001). CE studies were more likely to receive government funding (P = 0.003) and less likely to receive industry funding (P = 0.01), with 71.8% of CE studies primarily funded by a noncommercial source. The types of interventions studied differed based on funding source, with 95.4% of industry trials studying a drug or device. In addition, industry-funded CE studies were associated with the fewest pediatric subjects (P<0.001), the largest anticipated sample size (P<0.001), and the shortest study duration (P<0.001).
Conclusions
In this sample of studies examining high priority areas for CE research, less than a quarter are CE studies and the majority is supported by government and nonprofits. The low prevalence of CE research exists across CE studies with a broad array of interventions and characteristics.National Library of Medicine (U.S.) (5G08LM009778)National Institutes of Health (U.S.
sj-docx-2-dhj-10.1177_20552076241249286 - Supplemental material for Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records
No full textSupplemental material, sj-docx-2-dhj-10.1177_20552076241249286 for Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records by Christina Felix, Joshua D Johnston, Kelsey Owen, Emil Shirima, Sidney R Hinds, Kenneth D Mandl, Alex Milinovich and Jay L Alberts in DIGITAL HEALTH</p
sj-docx-1-dhj-10.1177_20552076241249286 - Supplemental material for Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records
No full textSupplemental material, sj-docx-1-dhj-10.1177_20552076241249286 for Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records by Christina Felix, Joshua D Johnston, Kelsey Owen, Emil Shirima, Sidney R Hinds, Kenneth D Mandl, Alex Milinovich and Jay L Alberts in DIGITAL HEALTH</p
Premarket Safety and Efficacy Studies for ADHD Medications in Children
No full textBackground: Attention-deficit hyperactivity disorder (ADHD) is a chronic condition and pharmacotherapy is the mainstay of treatment, with a variety of ADHD medications available to patients. However, it is unclear to what extent the long-term safety and efficacy of ADHD drugs have been evaluated prior to their market authorization. We aimed to quantify the number of participants studied and their length of exposure in ADHD drug trials prior to marketing. Methods: We identified all ADHD medications approved by the Food and Drug Administration (FDA) and extracted data on clinical trials performed by the sponsor and used by the FDA to evaluate the drug’s clinical efficacy and safety. For each ADHD medication, we measured the total number of participants studied and the length of participant exposure and identified any FDA requests for post-marketing trials. Results: A total of 32 clinical trials were conducted for the approval of 20 ADHD drugs. The median number of participants studied per drug was 75 (IQR 0, 419). Eleven drugs (55%) were approved after <100 participants were studied and 14 (70%) after <300 participants. The median trial length prior to approval was 4 weeks (IQR 2, 9), with 5 (38%) drugs approved after participants were studied <4 weeks and 10 (77%) after <6 months. Six drugs were approved with requests for specific additional post-marketing trials, of which 2 were performed. Conclusions: Clinical trials conducted for the approval of many ADHD drugs have not been designed to assess rare adverse events or long-term safety and efficacy. While post-marketing studies can fill in some of the gaps, better assurance is needed that the proper trials are conducted either before or after a new medication is approved.Version of Recor
Longitudinal histories as predictors of future diagnoses of domestic abuse: modelling study
No full texthttp://www.bmj.com/content/339/bmj.b3677Objective To determine whether longitudinal data in
patients’ historical records, commonly available in
electronic health record systems, can be used to predict a
patient’s future risk of receiving a diagnosis of domestic
abuse.
Design Bayesian models, known as intelligent histories,
used to predict a patient’s risk of receiving a future
diagnosis of abuse, based on the patient’s diagnostic
history. Retrospective evaluation of the model’s
predictions using an independent testing set.
Setting A state-wide claims database covering six years of
inpatient admissions to hospital, admissions for
observation, and encounters in emergency departments.
Population All patients aged over 18 who had at least four
years between their earliest and latest visits recorded in
the database (561 216 patients).
Main outcome measures Timeliness of detection,
sensitivity, specificity, positive predictive values, and
area under the ROC curve.
Results 1.04% (5829) of the patients met the narrow case
definition for abuse, while 3.44% (19 303) met the
broader case definition for abuse. The model achieved
sensitive, specific (area under the ROC curve of 0.88), and
early (10-30 months in advance, on average) prediction of
patients’ future risk of receiving a diagnosis of abuse.
Analysis of model parameters showed important
differences between sexes in the risks associated with
certain diagnoses.
Conclusions Commonly available longitudinal diagnostic
data can be useful for predicting a patient’s future risk of
receiving a diagnosis of abuse. This modelling approach
could serve as the basis for an early warning system to
help doctors identify high risk patients for further
screening.National Library of Medicine (grants R01 LM009879, R01 LM007677, and G08LM009778)Centers for Disease Control and Prevention (U.S.) (grant R01 PH000040
Breaking the news or fueling the epidemic? Temporal association between news media report volume and opioid-related mortality
No full textBackground
Historical studies of news media have suggested an association between reporting and increased drug abuse. Period effects for substance use have been documented for different classes of legal and illicit substances, with the suspicion that media publicity may have played major roles in their emergence. Previous analyses have drawn primarily from qualitative evidence; the temporal relationship between media reporting volume and adverse health consequences has not been quantified nationally. We set out to explore whether we could find a quantitative relationship between media reports about prescription opioid abuse and overdose mortality associated with these drugs. We assessed whether increases in news media reports occurred before or after increases in overdose deaths.
Methodology/Principal Findings
Our ecological study compared a monthly time series of unintentional poisoning deaths involving short-acting prescription opioid substances, from 1999 to 2005 using multiple cause-of-death data published by the National Center for Health Statistics, to monthly counts of English-language news articles mentioning generic and branded names of prescription opioids obtained from Google News Archives from 1999 to 2005. We estimated the association between media volume and mortality rates by time-lagged regression analyses. There were 24,272 articles and 30,916 deaths involving prescription opioids during the seven-year study period. Nationally, the number of articles mentioning prescription opioids increased dramatically starting in early 2001, following prominent coverage about the nonmedical use of OxyContin. We found a significant association between news reports and deaths, with media reporting preceding fatal opioid poisonings by two to six months and explaining 88% (p<0.0001, df 78) of the variation in mortality.
Conclusions/Significance
While availability, structural, and individual predispositions are key factors influencing substance use, news reporting may enhance the popularity of psychoactive substances. Albeit ecological in nature, our finding suggests the need for further evaluation of the influence of news media on health. Reporting on prescription opioids conforms to historical patterns of news reporting on other psychoactive substances.National Library of Medicine (U.S.) (R21LM009263-01)National Institutes of Health (U.S.)Canadian Institutes for Health ResearchUNC-GSK Center of Excellence in Pharmacoepidemiology and Public HealthGoogle (Firm
Accuracy of ICD-10 Codes for Suicidal Ideation and Action in Pediatric Emergency Department Encounters
No full textObjectives: According to the ideation-to-action framework of suicidality, suicidal ideation and suicidal action arise via distinct trajectories. Studying suicidality under this framework requires accurate identification of both ideation and action. We sought to assess the accuracy of International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for suicidal ideation and action in emergency department encounters. Methods: Accuracy of ICD-10-CM coding for suicidality was assessed through chart review of clinical notes for 205 emergency department encounters among patients 6–18 years old at a large academic pediatric hospital between June 1, 2016 and June 1, 2022. Physician notes were reviewed for documentation of past or present suicidal ideation, suicidal action, or both. The study cohort consisted of 103 randomly selected “cases,” or encounters assigned at least 1 ICD-10-CM code for suicidality, and 102 propensity-matched “noncases” lacking ICD-10-CM codes. Accuracy of ICD-10-CM codes was assessed using sensitivity, specificity, positive predictive value, and negative predictive value. Results: Against a gold standard chart review, the positive predictive value for ICD-10-CM suicidality codes was 86.9% (95% confidence interval [CI]: 84.5%–89.3%), and the negative predictive value was 76.2% (95% CI: 73.2%–79.2%). Nearly half of encounters involving suicidality were not captured by ICD-10-CM coding (sensitivity = 53.4%; 95% CI: 49.9%–56.9%). Sensitivity was higher for ideation-present (82.4%, 95% CI: 77.7%–87.1%) than for action-present (33.7%, 95% CI: 27.9%–39.5%) or action-past (20.4%, 95% CI: 15.5%–25.3%). Conclusions: Many cases of suicidality may be missed by relying on only ICD-10-CM codes. Accuracy of ICD-10-CM codes is high for suicidal ideation but low for action. To scale the ideation-to-action model for use in large populations, better data sources are needed to identify cases of suicidal action
The SMART Platform: early experience enabling substitutable applications for electronic health records
No full textObjective The Substitutable Medical Applications, Reusable Technologies (SMART) Platforms project seeks to develop a health information technology platform with substitutable applications (apps) constructed around core services. The authors believe this is a promising approach to driving down healthcare costs, supporting standards evolution, accommodating differences in care workflow, fostering competition in the market, and accelerating innovation.
Materials and methods The Office of the National Coordinator for Health Information Technology, through the Strategic Health IT Advanced Research Projects (SHARP) Program, funds the project. The SMART team has focused on enabling the property of substitutability through an app programming interface leveraging web standards, presenting predictable data payloads, and abstracting away many details of enterprise health information technology systems. Containers—health information technology systems, such as electronic health records (EHR), personally controlled health records, and health information exchanges that use the SMART app programming interface or a portion of it—marshal data sources and present data simply, reliably, and consistently to apps.
Results The SMART team has completed the first phase of the project (a) defining an app programming interface, (b) developing containers, and (c) producing a set of charter apps that showcase the system capabilities. A focal point of this phase was the SMART Apps Challenge, publicized by the White House, using http://www.challenge.gov website, and generating 15 app submissions with diverse functionality.
Conclusion Key strategic decisions must be made about the most effective market for further disseminating SMART: existing market-leading EHR vendors, new entrants into the EHR market, or other stakeholders such as health information exchanges.United States. Dept. of Health and Human Services. Office of the National Coordinator for Health Information Technology (Strategic Health IT Advanced Research Projects Award 90TR000101
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